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Duration 21 hours
Course Outline
- Distributed Computing in Large-Scale Data Environments
- Data extraction techniques (individual model training and distributed inference: conventional machine learning algorithms and MapReduce-based prediction) /li>
- Apache Spark MLlib integration
- Recommendation Systems and Targeted Advertising
- Fundamentals of natural language processing /li>
- Text clustering, categorization (labeling), and synonym identification
- Reconstruction of user profiles and tagging frameworks /li>
- Strategies for optimizing recommendation algorithms /li>
- Intra-class and inter-class lift analysis; methods for achieving precision /li>
- Establishing closed-loop feedback mechanisms for recommendation algorithms
- Logistic Regression and Ranking SVM techniques /li>
- Feature identification: (automated feature recognition utilizing deep learning and graph structures) /li>
- Natural Language Processing
- Chinese word segmentation techniques /li>
- Topic modeling (text clustering applications) /li>
- Text classification methodologies /li>
- Keyword extraction processes /li>
- Semantic analysis: semantic parsers and Word2Vec vector representations /li>
- Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) architectures
Requirements
No specific prerequisites are required for participation in this course.
Testimonials (1)
This is one of the best hands-on with exercises programming courses I have ever taken.
